Dyslexia Prediction using Deep Learning

Dyslexia, a neurodevelopmental disorder affecting reading and writing skills, poses significant challenges for individuals and educators alike. Early identification and intervention are important to reduce the impact on academic and personal development. This project focuses on leveraging deep learning techniques for the prediction of dyslexia, aiming to provide a reliable and efficient tool for early identification. The proposed deep learning model utilizes a diverse dataset comprising linguistic and cognitive features, including but not limited to phonological awareness, rapid automatized naming, and working memory. Through a carefully designed neural network architecture, the model learns intricate patterns associated with dyslexia, allowing for accurate prediction. Our approach involves preprocessing and feature engineering to enhance the model’s understanding of dyslexia-related factors. The model is trained on a comprehensive dataset, and its performance is evaluated using various metrics such as sensitivity, specificity, and accuracy. Additionally, interpretability methods are employed to enhance the transparency of the model’s decision-making process, providing insights into the key factors contributing to dyslexia prediction. The significance of this project lies in its potential to provide a non-invasive, cost-effective, and scalable solution for dyslexia prediction. By incorporating state-of-the-art deep learning techniques, we aim to contribute to the advancement of early intervention strategies and support systems for individuals at risk of dyslexia. The outcomes of this research have the potential to positively impact education systems, fostering a more inclusive environment for learners with dyslexia.